Automatic Crowd Sensing and Reporting System for Road Incidents

ABSTRACT

An automatic external incident detection and reporting system for a vehicle includes at least one of a plurality of cameras and a plurality of proximity sensors, an incident determination unit, a remote vehicle or object position determination unit, and a communication unit. The incident determination unit is configured to receive signals from the at least one of the cameras and proximity sensors, detect whether an external incident has occurred, and determine a type of incident. The remote vehicle or object position determination unit is configured to receive signals from the at least one of the cameras and proximity sensors and determine an incident location. The communication unit is configured to transmit at least the incident location and the type of incident to remote vehicles, to a local access point, or to share the incident location and the type of incident through a crowd-sourcing manner if the external incident has occurred.

FIELD

The present disclosure relates to automatic sensing by vehicles and, inparticular, an automatic incident detection and reporting system.

BACKGROUND

This section provides background information related to the presentdisclosure which is not necessarily prior art.

Many vehicles currently employ cameras and/or proximity sensors used forthe vehicle's own benefits. The cameras and/or proximity sensors provideinformation to the vehicle's control system regarding the events andenvironment surrounding the vehicle. The vehicle then may use thisinformation in executing its own systems and functions, such asautomatic emergency brake, forward collision warning, lane departurewarning, lane change assist, etc.

SUMMARY

This section provides a general summary of the disclosure, and is not acomprehensive disclosure of its full scope or all of its features.

An example automatic incident detection and reporting system for avehicle according to the present disclosure may include at least one ofa plurality of cameras and a plurality of proximity sensors, an incidentdetermination unit, a remote vehicle or object position determinationunit, and a communication unit. The incident determination unit isconfigured to receive signals from the at least one of the plurality ofcameras and the plurality of proximity sensors, detect whether anexternal incident has occurred, and determine a type of incident. Theremote vehicle or object position determination unit is configured toreceive signals from the at least one of the plurality of cameras andthe plurality of proximity sensors and determine an incident location.The communication unit is configured to transmit at least the incidentlocation and the type of incident to remote vehicles or a local accesspoint, or to share the incident location and the type of incidentthrough a crowd-sourcing manner if the external incident has occurred.

The incident determination unit may be configured to use facialrecognition, hand signals, or body language to determine whether adriver is in distress.

The incident determination unit may be configured to analyze images fromthe plurality of cameras to determine whether there has been a trafficaccident.

The incident determination unit may be configured to either compareimages from the plurality of cameras with known images of large animalsto determine whether there is large animal activity or use machinelearning methods and models to automatically identify one or moreanimals.

The incident determination unit may be configured to either compareimages from the plurality of cameras with known images of an acceptableroadway to determine whether there are potholes or debris on a roadwayor use machine learning methods and models to automatically identify aroad surface and any debris or damage to the road surface.

The external incident may be one of a vehicle accident, an immobilizedvehicle, a broken-down vehicle, a pothole, large animal activity, anoutlaw vehicle, a driver or other person in distress, significant damageto a road, debris, and one or more persons loitering, and the externalincident may involve only one or more vehicles, persons, animals orobjects outside of the vehicle.

The automatic incident detection and reporting system may furtherinclude a vehicle position determination unit that is configured todetermine a current position of the vehicle.

The automatic incident detection and reporting system may furtherinclude a driver alert system that is configured to alert a driver whenan incident is detected by the incident determination unit.

The communication unit may be configured to transmit the incidentlocation and type of incident to a data transmission system on thevehicle, and the data transmission system may communicate the incidentlocation and type of incident to a remote server.

The communication unit may be configured to transmit the incidentlocation and type of incident to an emergency services provider.

An example method according to the present disclosure for automaticallydetecting and reporting external incidents includes receiving, by anincident determination unit and a remote vehicle or object positiondetermination unit, a plurality of signals from at least one of aplurality of cameras and a plurality of proximity sensors disposed on avehicle; detecting, by the incident determination unit, whether anexternal incident has occurred; determining, by the incidentdetermination unit, a type of incident; determining, by the remotevehicle or object position determination unit, an incident location; andtransmitting, by a communication unit, at least the incident locationand the type of incident to remote vehicles or a local access point, orsharing, by the communication unit, the incident location and the typeof incident through a crowd-sourcing manner if the external incident hasoccurred.

The method may further include performing, by the incident determinationunit, facial recognition, hand-signal recognition, or body languagerecognition to determine whether a driver is in distress.

The method may further include analyzing, by the incident determinationunit, images from the plurality of cameras to determine whether therehas been a traffic accident.

The method may further include either comparing, by the incidentdetermination unit, images from the plurality of cameras with knownimages of large animals to determine whether there is large animalactivity, or employing, by the incident determination unit, detectionalgorithms to determine whether there is large animal activity.

The method may further include either comparing, by the incidentdetermination unit, images from the plurality of cameras with knownimages of an acceptable roadway to determine whether there are potholesor debris on the roadway, or employing, by the incident determinationunit, scene analysis algorithms to detect potholes or debris.

The method may further include an external incident that is one of avehicle accident, an immobilized vehicle, a broken-down vehicle, a majorpothole, large animal activity, an outlaw vehicle, a driver or otherperson in distress, significant damage to a road, and debris on theroad, and the external incident involves only one or more vehicles,persons, or objects outside of the vehicle.

The method may further include determining, by a vehicle positiondetermination unit, a current position of the vehicle.

The method may further include alerting, by a driver alert system, adriver when an incident is detected by the incident determination unit.

The method may further include transmitting, by the communication unit,the incident location and type of incident to a data transmission systemon the vehicle, and communicating, by the data transmission system, theincident location and type of incident to a remote server.

The method may further include transmitting, by the communication unit,the incident location and type of incident to an emergency servicesprovider.

Further areas of applicability will become apparent from the descriptionprovided herein. The description and specific examples in this summaryare intended for purposes of illustration only and are not intended tolimit the scope of the present disclosure.

DRAWINGS

The drawings described herein are for illustrative purposes only ofselected embodiments and not all possible implementations, and are notintended to limit the scope of the present disclosure.

FIG. 1 illustrates a vehicle including an external incident detectionand reporting system according to the present disclosure.

FIG. 2 is a schematic view of the external incident detection andreporting system of FIG. 1.

FIG. 3 is an illustration of several vehicles, where two vehicles areinvolved in an accident and at least one vehicle includes the externalincident detection and reporting system of FIG. 1.

FIG. 4 is a schematic view of the external incident detection andreporting system of FIG. 1 interacting with external vehicle andnon-vehicle systems.

FIG. 5 is a flow chart illustrating an example method of detecting andreporting external incidents according to the present disclosure.

FIG. 6 is a flow chart illustrating an example method of receiving andreporting external incidents at a fog node or cloud according to thepresent disclosure.

FIG. 7 is a flow chart illustrating an example method for outlaw vehicledetection reporting according to the present disclosure.

FIG. 8 is a flow chart illustrating an example method of receiving andreporting external incidents at a fog node or cloud according to thepresent disclosure.

FIG. 9 is a flow chart illustrating an example method for real-timelocating of an emergency vehicle according to the present disclosure.

Corresponding reference numerals indicate corresponding parts throughoutthe several views of the drawings.

DETAILED DESCRIPTION

Example embodiments will now be described more fully with reference tothe accompanying drawings.

Vehicles are often equipped with various sensors (for example, cameras,LiDAR, sonar, radar, microphones, etc.) to support safety and navigationfeatures on the vehicle. The present disclosure uses these sensors todetect incidents outside of the vehicle (for example, incidentsinvolving one or more remote vehicles, persons, etc.) and share thedetected incidents to other drivers and the community. Such incidentsmay include accidents, immobilized vehicles, broken-down vehicles, majorpotholes, large animal activity near the road, outlaw (amber alert orsilver alert) vehicle detection, drivers or people in distress (forexample, using facial recognition, body posture, hand signals/gestures,etc.), significant damage or debris on the road/side of road, severeweather, etc. The system of the present disclosure automatically detectsthe various incidents and shares them in a crowd source manner (forexample, utilizing fog nodes and cloud systems). Other vehicles,authorities, and service providers (for example, insurance companies)may then utilize the reported information. For example, the informationmay be used to dispatch help if needed. In some cases, the informationmay be stored and combined with other vehicles' data to build a completestory of time-stamped, specific events surrounding the incident.Furthermore, the information gathered may be used in automatic dataanalysis to determine which areas and at which times are more prone toaccidents, more likely to see large animal activity, etc.

With reference to FIG. 1, a vehicle 10 including an automatic externalincident detection and reporting system 12 according to the presentteachings is illustrated. Although the vehicle 10 is illustrated as anautomobile in FIG. 1, the present teachings apply to any other suitablevehicle, such as a sport utility vehicle (SUV), a mass transit vehicle(such as a bus), or a military vehicle, as examples. Additionally, thesystem 12 may be incorporated in any stationary structure or movingstructure as well. The system 12 is configured to automatically detectvarious incidents (for example only, accidents, immobilized vehicles,broken-down vehicles, major potholes, large animal activity near theroad, amber alert or silver alert vehicle detection, drivers or peoplein distress, significant damage or debris on the road/side of road,severe weather, etc.) and share them in a crowd source manner (forexample, utilizing cloud systems). The system 12 may generally includeone or more proximity and/or environmental sensors 20, a driver alertsystem 22, a dedicated short range communication (DSRC) system 24, acontroller 26, a global positioning system (GPS) or global navigationsatellite system (GNSS) 28, one or more cameras 30, and a datatransmission system 32. While a DSRC system 24 is described herein, itis understood that the system 12 is not limited to utilizing a DSRCsystem 24. For example, the DSRC system 24 may be any V2X communicationprotocol or any cellular protocol.

The controller 26 can be any suitable controller for monitoring and/orcontrolling one or more of the proximity and/or environmental sensors20, the driver alert system 22, the DSRC system 24, the GPS/GNSS 28, oneor more of the cameras 30, the data transmission system 32 and/or theadditional vehicle systems, sensors, and functions. In this application,including the definitions below, the terms “controller” and “system” mayrefer to, be part of, or include processor hardware (shared, dedicated,or group) that executes code and memory hardware (shared, dedicated, orgroup) that stores code executed by the processor hardware. The code isconfigured to provide the features of the controller and systemsdescribed herein.

The proximity sensors 20 include one or more sensors configured toidentify and/or detect the presence of objects, such as pedestrians,cyclists, or other vehicles, in one or more areas around the subjectvehicle 10. The proximity sensors 20 can include any suitable sensors,such as any suitable radar, sonar, laser, camera, ultrasonic, LiDAR,stereo, or other suitable sensors for detecting objects in an areaaround the subject vehicle 10. The environmental sensors 20 can includesensors to determine light level, weather data, temperature, roadsurface status, traffic conditions, lane markers, etc. The proximityand/or environmental sensors 20 can be mounted at any suitable positionon the subject vehicle 10, such as in the front of the subject vehicle10, rear of the subject vehicle 10, near the front corners of thesubject vehicle 10, near the back corners of the subject vehicle 10, oralong the sides of the subject vehicle 10.

The one or more cameras 30 include one or more cameras configured toidentify and/or detect the presence of objects, such as pedestrians,cyclists, or other vehicles, in one or more areas around the subjectvehicle 10. The cameras 30 may also be used to record and/or detect thevarious incidents outside of and around the vehicle (for example,incidents involving one or more remote vehicles, persons, etc.). Suchincidents may include accidents, immobilized vehicles, broken-downvehicles, major potholes, large animal activity near the road, outlaw(amber alert or silver alert) vehicle detection, drivers or people indistress (for example, using facial recognition, body posture, handsignals/gestures, etc.), significant damage or debris on the road/sideof road, sever weather, etc. The cameras 30 can include any suitablecamera for detecting objects or incidents in an area around the subjectvehicle 10 (for example only, audio, video, LiDAR, radar, sonar etc.).The cameras 30 can be mounted at any suitable position on the subjectvehicle 10, such as in the front of the subject vehicle 10, rear of thesubject vehicle 10, near the front corners of the subject vehicle 10,near the back corners of the subject vehicle 10, or along the sides ofthe subject vehicle 10.

Now referring to FIGS. 2-4, the system 12 is configured to automaticallydetect various incidents (for example only, accidents, immobilizedvehicles, broken-down vehicles, major potholes, large animal activitynear the road, amber alert or silver alert vehicle detection, drivers orpeople in distress, significant damage or debris on the road/side ofroad, severe weather, etc.) and share them in a crowd source manner (forexample, utilizing fog nodes or cloud systems).

The controller 26 may include a vehicle position determination unit 36,an incident determination unit 40, a remote vehicle or object positiondetermination unit 44, and a communication unit 48. The vehicle positiondetermination unit 36 may communicate with, and receive signals from,the GPS and/or GNSS 28 and/or the proximity and/or environmental sensors20 and cameras 30 to determine a location of the vehicle 10. Theproximity and/or environmental sensors 20 and cameras 30 may communicatewith the vehicle position determination unit 36 through a ControllerArea Network (CAN) system 52 or any other in-vehicle communicationnetwork.

The incident determination unit 40 may receive signals from theproximity and/or environmental sensors 20 and cameras 30. The incidentdetermination unit 40 may use the proximity and/or environmental sensors20 and cameras 30 to determine whether an incident has occurred outsideof and around the vehicle (for example, incidents involving one or moreremote vehicles, persons, etc.). The incident determination unit 40 mayalso determine a location and a type of external incident from the datatransmitted from the proximity and/or environmental sensors 20 andcameras 30. Such incidents may include accidents, immobilized vehicles,broken-down vehicles, major potholes, large animal activity near theroad, outlaw (amber alert or silver alert) vehicle detection, drivers orpeople in distress (for example, using facial recognition, body posture,hand signals/gestures, etc.), significant damage or debris on theroad/side of road, severe weather, etc.

For example, the incident determination unit 40 may determine that anaccident has occurred by analyzing images from the cameras 30 to detectremote vehicle damage, remote vehicle status (such as whether driving,position of the vehicle relative to the road, location of the vehicle,etc.), status of driver (such as whether the driver is in the vehicle,out of the vehicle, alert, etc.), etc. In addition to the cameras 30, orin the alternative, the incident determination unit 40 may utilize theproximity and/or environmental sensors 20 to determine the information.

For example, the incident determination unit 40 may determine that animmobilized vehicle or broken-down vehicle is present by analyzingimages from the cameras 30 to detect remote vehicle status (such aswhether driving, position of the vehicle relative to the road, locationof the vehicle, etc.), status of driver (such as whether the driver isin the vehicle, out of the vehicle, alert, etc.), etc. In addition tothe cameras 30, or in the alternative, the incident determination unit40 may utilize the proximity and/or environmental sensors 20 todetermine the information.

For example, in some embodiments, the incident determination unit 40 maydetermine the occurrence of extreme road conditions, including majorpotholes, significant damage, or debris on the road/side of road, byanalyzing images from the cameras 30 to detect features of the road orobjects on the road that are unexpected. For example, the incidentdetermination unit 40 may compare the images from the cameras 30 withimages of known objects that may be on the road (for example road cones,barrels, signs, construction equipment, etc.). If the images from thecameras 30 do not match any of the images of known objects, then theincident determination unit 40 may determine that there is an occurrenceof potholes, significant damage, or debris on the road/side of road. Inaddition to the cameras 30, or in the alternative, the incidentdetermination unit 40 may utilize the proximity and/or environmentalsensors 20 to determine the information.

Further, in some example embodiments, the incident determination unit 40may detect the extreme road conditions by utilizing machine learningmethods and models to automatically identify a road surface and anydebris or damage to the road surface. For example, the incidentdetermination unit 40 may use background detection and subtractiontechniques to extract the foreground objects such as an adaptive Kalmanfilter that tracks objects from frame to frame, kernel density methods,mixture of gaussians, principal component analysis, and deep learningmethods. Once the background is separated from the foreground objects,the objects are recognized using image recognition methods such as deeplearning. The same is done for the background in order to identifycertain features: quality and type of pavement, soil, vegetation, andother objects near and around the road/ground.

For example, the incident determination unit 40 may determine severweather conditions or localized storms (i.e., monsoons, hail, tornadoes,etc.) by analyzing images from the cameras 30 to detect features of thecurrent weather that match known images from different types of storms.For example, the incident determination unit 40 may compare the imagesfrom the cameras 30 with images of hail, tornado funnels, or monsoons.If the images from the cameras 30 match any of the known or storedimages, then the incident determination unit 40 may identify extremeweather or localized storms. In addition to the cameras 30, or in thealternative, the incident determination unit 40 may utilize theproximity and/or environmental sensors 20 to determine the information.

For example, in some embodiments, the incident determination unit 40 maydetermine the occurrence of large animal activity near the road byanalyzing images from the cameras 30 to detect large animals. Forexample, the incident determination unit 40 may compare the images fromthe cameras 30 with images of known animals that may be on the road (forexample deer, moose, cow, horse, pig, dog, etc.). If the images from thecameras 30 match any of the images of known objects, then the incidentdetermination unit 40 may determine that there is an occurrence of largeanimal activity near or on the road. In addition to the cameras 30, orin the alternative, the incident determination unit 40 may utilize theproximity and/or environmental sensors 20 to determine the information.

Further, in some example embodiments, the incident determination unit 40may determine the occurrence of large animal activity near the road byutilizing machine learning methods and models to automatically identifyone or more animals. For example, the incident determination unit 40 mayuse background detection and subtraction techniques to extract theforeground objects such as an adaptive Kalman filter that tracks objectsfrom frame to frame, kernel density methods, mixture of gaussians,principal component analysis, and deep learning methods. Once thebackground is separated from the foreground objects, the objects arerecognized using image recognition methods such as deep learning. Thesame is done for the background in order to identify certain features:quantity and types of animals or other objects near and around theroad/ground.

For example, the incident determination unit 40 may detect outlaw (forexample, amber alert, silver alert, or otherwise wanted) vehicles byanalyzing images from the cameras 30 to detect remote vehicles. Forexample, the incident determination unit 40 may compare the images fromthe cameras 30 with a database of vehicle information (for example,make, model, and color of vehicle or license plate number). The databaseof vehicle information may be communicated to the system 12, andincident determination unit 40, from a government alert sent to thevehicle 10, either through wireless, Bluetooth, cloud, fog nodes, orother communications. If the images from the cameras 30 include vehiclesthat match any of the entries in the database, then the incidentdetermination unit 40 may determine that an outlaw vehicle has beendetected. In addition to the cameras 30, or in the alternative, theincident determination unit 40 may utilize the proximity and/orenvironmental sensors 20 to determine the information.

For example, the incident determination unit 40 may detect drivers orpeople in distress by analyzing images from the cameras 30 to detectpeople. For example, the incident determination unit 40 may compare theimages from the cameras 30 with images of known facial characteristicsindicating distress (for example, furrowed eyebrows, tears, scowl,yelling, etc.) using facial recognition. The incident determination unit40 may further compare the images from the cameras 30 with images ofknown body posture characteristics (for example, both arms in air,kneeled by vehicle, etc.) indicating distress or hand signals (i.e.,body language recognition) or gestures (crossing arms above head,waving, thumbs up, etc.) indicating distress (i.e., hand-signalrecognition). If the images from the cameras 30 match any of thecharacteristics, hand signals, or gestures of distress, then theincident determination unit 40 may determine that one or more people indistress are present.

In addition to the proximity and/or environmental sensors 20 and cameras30, the incident determination unit 40 may communicate with the DSRCsystem 24 (or any other V2X communication or cellular protocol) todetermine remote vehicle and/or infrastructure information. For example,the DSRC system 24 (or any other V2X communication or cellular protocol)may communicate with a remote vehicle to determine whether an accidenthas occurred or whether there is an immobilized vehicle or broken-downvehicle in the vicinity. For example, the remote vehicle involved in theaccident or that is immobilized or broken-down may communicate itsstatus to the vehicles DSRC system 24 (or any other V2X communication orcellular protocol).

In addition, or alternatively, the incident determination unit 40 maycommunicate with a driver's or passenger's device 56 either wirelesslyor through wired or Bluetooth communications. The driver's orpassenger's device 56 may include a mobile phone, a tablet, a laptopcomputer, or another electronic device. The driver's or passenger'sdevice 56 may transmit camera images or remote vehicle data to theincident determination unit 40 for use similar to the proximity and/orenvironmental sensors 20, cameras 30, and DSRC system 24.

The remote vehicle or object position determination unit 44 maydetermine GPS and/or GNSS position coordinates, or other location data,for the remote vehicle or person in distress. For example, the remotevehicle or object position determination unit 44 may receive data fromthe proximity and/or environmental sensors 20, cameras 30, and DSRCsystem 24 and use the images, sensor data, or messages to determine oneor more of GPS and/or GNSS position coordinates, street location, milemarker location, etc., of the remote vehicles or person in distress.

The communication unit 48 may receive data from the vehicle positiondetermination unit 36, the incident determination unit 40, and theremote vehicle or object position determination unit 44 and communicateit to the data transmission system 32 or the driver alert system 22. Forexample, if there is a detected incident outside of and around thevehicle (for example, incidents involving one or more remote vehicles,persons, etc.), the communication unit 48 may receive data indicating aposition or location of the vehicle 10 from the vehicle positiondetermination unit 36, the detected incident from the incidentdetermination unit 40, and a location or position of the remotevehicle/incident/person from the remote vehicle or object positiondetermination unit 44.

The driver alert system 22 may receive commands from the controller 26to alert the driver. The driver alert system 22 may alert the driver byany means available, including but not limited to a driver informationcenter (DIC) display, a heads-up display, an alarm, a radio, a steeringwhile (for example with vibration), or a driver seat (for example withvibration).

The data transmission system 32 may receive commands from the controller26 to distribute data to remote vehicle, mobile device, or stationarydevice 10′ or remote server, cloud server, or fog node 60. The cloud/fogsystem 60 may then distribute the information to one or more remotevehicles, mobile devices (for example, mobile phones, navigationsystems, etc.), or stationary devices (for example only, intersectioncameras, security cameras, etc.) 10″ not in the local area (as opposedto vehicles, mobile devices, or stationary devices 10′) and/or emergencyor other services 64 (FIG. 4). The emergency or other services 64 mayinclude police, fire, ambulance, etc., emergency services, insuranceservices, doctor or other medical non-emergency services, game wardens,park rangers, non-emergency law enforcement services, etc.

The data transmission system 32 may transmit information such as theposition or location of the vehicle 10, the detected incident, and thelocation or position of the remote vehicle/incident/person. Additionalinformation that may either be transmitted or saved for futuretransmission may be photos of videos of the incident/vehicle/person foruse by emergency or other services.

In some examples, the controller 26 may include a memory 62 or otherelectronic storage to temporarily store or cache the incidentinformation for later distribution. Examples of information that may bestored include, but are not limited to, snapshots or images of theincidents or accidents, videos of the incidents or accidents, incidentlocation information, incident time information, weather metrics,details and statistics of large animal activities, a list of providers,services, or agencies (for example, emergency services—911, roadsideassistance, operator assistance, animal control, animal studies,research institutions, insurance companies, weather service, roadworkservices, or other providers).

An example vehicle accident scenario is illustrated in FIG. 3. In thedepicted example, vehicles 10 a and 10 b are involved in a trafficincident, in particular, a vehicle accident. In this scenario, thesystem 12 of the vehicle 10 would record data through the vehicle'scamera(s) 30 and proximity or environmental sensors 20 (along withtimestamp and GPS location). Data that may be recorded includes, but isnot limited to, snapshots or images of the incidents or accidents,videos of the incidents or accidents, incident location information,incident time information, weather metrics, details and statistics oflarge animal activities. The system 12 in the vehicle 10 may send theincident information immediately to emergency services through thecommunication unit 48, data transmission system 32, and cloud 60, aspreviously discussed, or the system 12 may store the data for laterquery in the memory 62. Additionally, remote vehicles, mobile devices,or stationary devices 10′ or road infrastructure 66 may record image orvideo clips of the accident along with timestamp and GPS informationwhich can be combined with the incident information collected by thesystem 12 to reconstruct the scene to assist police reporting and/orinsurance companies in determining facts and fault.

Now referring to FIG. 4, the system 12 of the host vehicle 10 uses thevarious sensors 20 and cameras 30 to detect incidents outside of thevehicle (for example, incidents involving one or more remote vehicles,persons, etc.) and share information related to the detected incidentsto other drivers and the community, either through local sharing (forexample, DSRC, V2X communications, cellular communications, etc.) withremote vehicles, mobile devices, or stationary devices 10′ in the localarea or remote vehicles, mobile devices, or stationary devices 10″ andservices through a crowd sourcing manner (for example, utilizing fognodes or cloud systems). Such incidents, as previously stated, mayinclude accidents, immobilized vehicles, broken-down vehicles, majorpotholes, large animal activity near the road, outlaw (amber alert orsilver alert) vehicle detection, drivers or people in distress (forexample, using facial recognition, body posture, hand signals/gestures,etc.), significant damage or debris on the road/side of road, etc.Examples of information that may be shared include, but are not limitedto, snapshots or images of the incidents or accidents, videos of theincidents or accidents, incident location information, incident timeinformation, weather metrics, details and statistics of large animalactivities, a list of providers, services, or agencies (for example,emergency services—911, roadside assistance, operator assistance, animalcontrol, animal studies, research institutions, insurance companies,weather service, roadwork services, or other providers).

The other vehicles, mobile devices, or stationary devices 10′, 10″,authorities 64, and service providers (for example, emergencyservices—911, roadside assistance, operator assistance, animal control,animal studies, research institutions, insurance companies, weatherservice, roadwork services, or other providers) 64 may then utilize thereported information. For example, the information may be used todispatch help if needed. In some cases, the information may be storedand combined with other vehicles', mobile devices', or stationarydevices' 10′ data to build a complete story of time-stamped, specificevents surrounding the incident (for example, the cloud 60 may stitchindividual videos uploaded by various vehicles 10, 10′, 10″, portabledevices such as cell phones, navigation systems, etc., and/or stationarydevices such as cameras mounted on structures or traffic lights, etc.).Furthermore, the information gathered may be used in automatic dataanalysis to determine which areas and at which times are more prone toaccidents, more likely to see large animal activity, etc.

As illustrated in FIG. 4, the host vehicle 10 may include the memory 62or other electronic storage to temporarily store or cache the incidentinformation (for example, snapshots or images of the incident, videos ofthe incident, incident location information, incident time information,weather metrics, details and statistics of large animal activities,etc.) for later distribution.

The host vehicle 10 may transmit the data through wireless, Bluetooth,DSRC, or other communications to remote vehicles, mobile devices, orstationary devices 10′ in the local area or to a local access point 68.The local access point 68 may be a transceiver in a wireless local areanetwork (WLAN) or a wireless access point (WAP). For example, the localaccess point 68 may be a road side unit, a fog node, an edge, a celltower, a wireless access (Wi-Fi access point), etc. The local accesspoint 68 may receive the data from the host vehicle 10, store the data,and transmit it to other users within the network (for example remotevehicles, mobile devices, or stationary devices 10′) or other accesspoints. For example, the local access point 68 may be used for temporarystorage or cache of the incident information. The local access point 68may also be used for long term (for example 6 months) storage of localstatistics (for example, weather and large animal activity).

The local access point 68 may also transmit the incident informationdata to the cloud server 60 or fog node. The cloud server may be alogical server that is built, hosted, and delivered through a cloudcomputing platform over the internet. Cloud servers may possess andexhibit similar capabilities and functionality to a physical hardwareserver but are accessed remotely in a shared “virtualized” environmentfrom a cloud service provider. Fog computing may act as a “mini-cloud,”located at the edge of the network and implemented through a variety ofedge devices, interconnected by a variety, mostly wireless,communication technologies. Thus, a fog node may be the infrastructureimplementing the said mini-cloud. For example, the fog node in thepresent example may be the local access point 68 that transmits datafrom the system 12 to the cloud 60.

The incident information data may be stored in the cloud 60 or fog nodelong-term (for example, forever or until decided as not needed by a dataretention policy). In addition to storage, the cloud 60 may calculatetrends and statistics from the stored data (for example weather andlarge animal activity trends or accident occurrence statistics for alocation).

Remote vehicles, mobile devices, or stationary devices 10″ may pull thedata or information from the cloud 60, for example, to prepare trafficroutes, avoid back-ups or collisions, or for safety. Additionally, thehistorical analysis of the data in the cloud 60 may be used to createwarnings at higher incident times of the day which can be sent tovehicles in the area. Emergency and other services 64 may also pull thedata from the cloud 60, for example, to dispatch emergency vehicles orgame wardens/park rangers, for insurance purposes, for accident reports,etc.

Now referring to FIG. 5, an example method 100 of detecting andreporting external incidents from a vehicle according to the presentdisclosure is illustrated. The method 100 starts at 104. At 108, themethod 100 receives notifications from the fog/cloud 60 system. Forexample, the notifications may include amber alerts, silver alerts, orother outlaw vehicle alerts sent by a government organization or thepolice. For example, the police or other government organization maystore databases of amber alerts, silver alerts, outlaw vehicle alerts,regional information, announcements from authorities, and other alertsin the cloud or remote server 60. The information may be sent to thesystem 12 (for example through wireless, Bluetooth, or othercommunications) from the cloud 60 through fog nodes or the local accesspoint 68 and stored in the memory 62.

At 112, the method 100 receives GPS and/or GNSS data and determinesvehicle position. Specifically, the vehicle position determination unit36, previously discussed, may communicate with, and receive signalsfrom, the GPS and/or GNSS 28 and/or the proximity and/or environmentalsensors 20 and cameras 30 to determine a location of the vehicle 10. Theproximity and/or environmental sensors 20 and cameras 30 may communicatewith the vehicle position determination unit 36 through a ControllerArea Network (CAN) system 52.

At 116, the method 100 receives data from the proximity and/orenvironmental sensors 20, the cameras 30, and the DSRC system 24.Specifically, the incident determination unit 40, previously discussed,may receive signals from the proximity and/or environmental sensors 20and cameras 30 which indicate information regarding the vehicles,structures, and environment surrounding the vehicle 10. In addition, oralternatively, the incident determination unit 40 may communicate with adriver's or passenger's device 56 either wirelessly or through wired orBluetooth communications. The driver's or passenger's device 56 mayinclude a mobile phone, a tablet, a laptop computer, or anotherelectronic device. The driver's or passenger's device 56 may transmitcamera images or remote vehicle data to the incident determination unit40 for use similar to the proximity and/or environmental sensors 20,cameras 30, and DSRC system 24.

At 120, the method 100 determines whether there have been any incidentsin the vehicle's proximity. For example, the incident determination unit40 may use the proximity and/or environmental sensors 20, cameras 30,DSRC system 24 communications, and/or driver's or passenger's device 56to determine whether an incident has occurred outside of and around thevehicle (for example, incidents involving one or more remote vehicles,persons, etc.). Such incidents may include accidents, immobilizedvehicles, broken-down vehicles, major potholes, large animal activitynear the road, outlaw (amber alert or silver alert) vehicle detection,drivers or people in distress (for example, using facial recognition,body posture, hand signals/gestures, etc.), significant damage or debrison the road/side of road, etc.

If there has not been an incident at 120, method 100 returns to 112. Ifan incident was detected at 120, the method determines the position ofthe remote vehicle involved in the incident at 124. Specifically, theremote vehicle positon or object determination unit 44 may determine GPSand/or GNSS position coordinates, or other location data, for the remotevehicle or person in distress. For example, the remote vehicle or objectposition determination unit 44 may receive data from the proximityand/or environmental sensors 20, cameras 30, and DSRC system 24 and usethe images, sensor data, or messages to determine one or more of GPSand/or GNSS position coordinates, street location, mile marker location,etc., of the remote vehicles or person in distress. Additionally, anyfurther images, video, and data may be collected from the cameras 30and/or sensors 20.

At 128, the method 100 transmits the incident information to remotevehicles or access points in the local area. Specifically, thecommunication unit 48, as previously described, may transmit the datathrough wireless, Bluetooth, DSRC, or other communications to remotevehicle, mobile device, or stationary device 10′ in the local area or toa local access point 68. The local access point 68 may be a transceiverin a wireless local area network (WLAN) or a wireless access point(WAP). For example, the local access point 68 may be a road side unit, afog node, an edge, a cell tower, a wireless access (Wi-Fi_33 accesspoint), etc. The local access point 68 may receive the data from thehost vehicle 10 and transmit it to other users within the network (forexample remote vehicles, mobile devices, or stationary devices 10′) orother access points.

At 132, the local access point 68 may transmit the data to the cloud(i.e., a remote server) 60 or fog node. The cloud server may be alogical server that is built, hosted, and delivered through a cloudcomputing platform over the internet. Cloud servers may possess andexhibit similar capabilities and functionality to a physical hardwareserver but are accessed remotely in a shared “virtualized” environmentfrom a cloud service provider. Fog computing may act as a “mini-cloud,”located at the edge of the network and implemented through a variety ofedge devices, interconnected by a variety, mostly wireless,communication technologies. Thus, a fog node may be the infrastructureimplementing the said mini-cloud. For example, the fog node in thepresent example may be the local access point 68 that transmits datafrom the system 12 to the cloud 60.

At 136, method 100 determines whether information has been requested byremote vehicles or services. If the information has not been requested,method 100 returns to 132. If the incident information has beenrequested, the incident information is transmitted to the remotevehicles, mobile devices, or stationary devices 10″, emergency serviceproviders, or other service providers at 140. Specifically, remotevehicles, mobile devices, or stationary devices 10″ and services mayreceive the information on the detected incidents, either through localsharing (DSRC, etc.) or through a crowd sourcing manner (for example,utilizing cloud systems). The other vehicles, mobile devices, orstationary devices 10″, authorities 64, and service providers (forexample, insurance companies) 64 may then utilize the reportedinformation. For example, the information may be used to dispatch helpif needed. In some cases, the information may be stored and combinedwith other vehicles', mobile devices', or stationary devices' 10′ datato build a complete story of time-stamped, specific events surroundingthe incident. Furthermore, the information gathered may be used inautomatic data analysis to determine which areas and at which times aremore prone to accidents, more likely to see large animal activity, etc.

The method ends at 144.

Now referring to FIG. 6, an example method 200 of receiving andreporting external incidents at a fog node or cloud according to thepresent disclosure is illustrated. Method 200 begins at 204. At 208, themethod 200 listens for incoming notifications. Specifically, thecloud/fog system 60 monitors wireless, Bluetooth, or othercommunications networks for incoming notifications.

At 212, the incoming notifications received by the cloud/fog system 60are filtered based on metadata and compiled into unique events. Forexample, the cloud/fog system 60 would analyze the metadata (i.e.,timestamp, location, description, snapshot, etc.) from each of theincoming notifications to group them into separate events.

At 216, the method 200 determines whether additional information isneeded for each of the events. For example, the cloud/fog system 60analyzes the notifications received in each of the separate events anddetermines whether there are notifications for a predetermined list ofinformation. The predetermined list of information may be different fordifferent types of events. An example list for a traffic accident mayinclude images of vehicles involved, images of license plates, images ofaccident, images of drivers, images of passengers, video of accident,weather data, time data, location data, road condition data, etc.

If additional information is needed at 216, method 200 requests moreinformation from notifiers at 220. For example, the incomingnotifications received in step 208 may include notifier information inthe metadata. The cloud/fog system 60 may extract the notifierinformation and contact the notifier requesting additional information.The additional information requested may be specific, such as a specificimage or data file, or the requested information may be a genericrequest.

At 224, the method 200 collects and analyzes the additional information.For example, the cloud/fog system 60 collects and filters the additionalinformation similar to steps 208 and 212. At 228, the notifiers whoresponded providing information are incentivized. For example, thenotifiers could receive credits that may be turned in for discounts orgift cards, or the notifiers may be awarded in another manner. Themethod 200 then moves to step 232.

If additional information was not needed at 216, method 200 determineswhether the system is operating in the fog node at 232 (i.e., as opposedto a cloud mode). As previously stated, fog computing may act as a“mini-cloud,” located at the edge of the network and implemented througha variety of edge devices, interconnected by a variety, mostly wireless,communication technologies. Fog computing may be faster and moreefficient than cloud computing because fog computing brings the cloudservice closer to “things” such as sensors, embedded systems, mobilephones, cars, etc. The fog node may be the specific edge device that isimplementing the “mini-cloud.”

If not operating in the fog node, method 200 stores the incidentinformation in the cloud 60 at 236. If operating in the fog node at 232,the method generates an event report at 240. The event report mayinclude all of the data collected in relation to the categorized eventand packaged for storage or transmission to a service or emergencyprovider.

At 244, the event report is sent to the cloud 60 for storage. The cloud60 may be utilized for long-term storage (for example, indefinitely oruntil a data retention policy ends). Service or other approved providersmay be able to access the event report from the cloud 60. For example,insurance providers may access the event report for accidentreconstruction, determination of fault, or fact-finding.

The method 200 ends at 248.

Now referring to FIG. 7, an example method 300 for outlaw vehicledetection reporting according to the present disclosure is illustrated.Method 300 begins at 304. At 308, notifications related to outlawvehicles are received. For example, the notifications may include amberalerts, silver alerts, or other outlaw vehicle alerts sent by agovernment organization or the police. The contents of the notificationmay include license plate numbers, vehicle identification numbers (VIN),vehicle (make, model, color, type, number of doors, etc.) descriptions,person (name, sex, age, height, weight, race, hair color, identifyingmarks, etc.) descriptions, etc. For example, the police or othergovernment organization may store databases of amber alerts, silveralerts, outlaw vehicle alerts, regional information, announcements fromauthorities, and other alerts in the cloud or remote server 60. Theinformation may be sent to the system 12 (for example through wireless,Bluetooth, or other communications) from the cloud 60 through fog nodesor the local access point 68 and stored in the memory 62.

At 312, data of the surrounding area is recorded. For example, data iscontinuously recorded by the cameras 30, proximity/environmental sensors20, and driver devices 56. The recorded data may include, but is notlimited to, snapshots or images, videos, location information, timestampinformation, weather metrics, etc.

The recorded data is pushed to a buffer at 312 for storage. For example,the image is pushed to the buffer for short-term local storage orcaching. The host vehicle 10 may include the memory 62 or otherelectronic storage to temporarily store or cache the information (forexample, snapshots or images, videos, location information, timeinformation, weather metrics, etc.) for later distribution. For exampleonly, the memory 62 may hold 32 GB of data which is split between storedfootage defined as incident footage and data that is continuouslyrecorded from the cameras 30, proximity sensors 20, and/or driver device56.

At 320, the method 300 determines whether the buffer is full. Forexample, to be effective for its specific intended use, the memory 62 isof a limited size. As such, the memory 62 only has the capability tostore a limited number of images and related data. For example, thememory 62 may have the capacity to store within a range of 1 gigabyte to1 terabyte of data. The space allotted to the continuously storedinformation may change based on whether any incidents have beenidentified and whether any incident data packages have been stored forlater distribution. For example only, the buffer may be full if 70% ofthe 32 GB is in use.

If the buffer is full, the oldest image is discarded at 324 to makespace for new images. For example, the images may be ordered accordingto the timestamp associated with each image. Thus, when the bufferbecomes full, the image having the oldest timestamp is discarded andreplaced with the image and data taken in step 312. If the buffer is notfull at 320, the method 300 returns to 312.

At 328, images taken at time interval t are evaluated. For example,while the cameras 30, proximity sensors 20, and/or driver devices 56 maybe continuously recording images or video, the incident determinationunit 40 may analyze image clips at specified time intervals. In somecases, time t may be every 6 seconds.

At 332, the timestamp, GPS data, and image for each of the imagesevaluated at time interval t is packaged. For example, for each of theimages taken, a timestamp and GPS location is recorded along with thephysical image. All of the data collected for each image may be packagedfor analysis.

At 336, the incident is classified. For example, the incidentdetermination unit 40 may analyze the data contents of the package fromstep 332 to determine an incident classification. As previouslydiscussed, the incident may be one of a vehicle accident, an immobilizedvehicle, a broken-down vehicle, a major pothole, large animal activity,an outlaw vehicle, a silver alert vehicle, an amber alert vehicle, adriver or other person in distress, significant damage to a road, anddebris on the road. The incident determination unit 40 may determine theincident classification through image comparisons, facial recognition,body language identification, hand signal identification, governmentalerts, vehicle databases, etc.

At 340, the method 300 may determine whether the vehicle is an outlawvehicle. An outlaw vehicle may be an amber alert vehicle, a silver alertvehicle, or another outlaw vehicle. For example, the incidentdetermination unit 40 may determine the outlaw vehicle through imagecomparisons, facial recognition, government alerts, vehicle identifierdatabases, etc.

If not an outlaw vehicle, the method 300 returns to 312. If an outlawvehicle at 340, the method 300 may generate a package including theimages and data for distribution at 344. For example, the communicationunit 48 may take multiple packages from step 332 that apply to the sameincident classification (i.e., that apply to the identified outlawvehicle) and format the contents and data for distribution.

At 348, the package is sent to the police department. As previouslydiscussed, the package may be sent to the police department through thefog node(s) or the cloud.

At 352, the package is stored within the vehicle and sent to and storedwithin the fog node or local access point 68. The fog node or localaccess point may provide temporary storage or caching (until the data issent to the cloud 60), or the fog node or local access point may providelong term (for example, months) storage of the local statistics(weather, large animal activity). The incident information data may bestored in the cloud 60 (for example, forever or until decided as notneeded by a data retention policy). In addition to storage, the cloud60, fog node, or local access point 68 may calculate trends andstatistics from the stored data.

The incident information data may be stored within the memory 62 of thevehicle 10. The memory 62 may provide temporary storage or caching untilthe data can be sent to the fog node, local access point 68, or cloud60. Additionally, the memory 62 may provide temporary storage until thedata is requested by police or other services.

At 356, the method 300 determines whether there has been a query for thepackage. The query may come from a service provider (for example,insurance, police, etc.) or from one of the individuals involved in theincident.

If a query was received, the package is sent to the party initiating thequery at 360. The method then ends at 364. If a query was not receivedat 356, the method 300 determines whether an elapsed time is greaterthan a threshold at 368. For example only, the elapsed time may equateto a document retention policy and may be equal to approximately 2years.

If the time is not greater than the threshold, method 300 returns to356. If the time is greater than the threshold at 368, the package isdeleted from the cloud at 372. The method then ends at 364.

Now referring to FIG. 8, an example method 400 of receiving andreporting external incidents at a fog node or cloud according to thepresent disclosure is illustrated. Method 400 starts at 404.

At 408, alerts are received from the government or police. For example,the government organization or police may upload or send featureinformation (for example only, car shape, make, model, year, color,license plate number, vehicle identification number, or otheridentifying characteristics) of the outlaw vehicle to the cloud 60.

At 412, the alerts are sent to local vehicles, mobile devices, orstationary devices 10′. For example, the fog nodes or local accesspoints 68 located around the estimated location of the suspect, oroutlaw, vehicle receive the data from the cloud 60 and distribute thedata to local remote vehicles, mobile devices, or stationary devices10′. For example only, if the estimated location of the suspect, oroutlaw, vehicle is a 60 mile radius area, the fog nodes or local accesspoints 68 within ten miles of the 60 mile radius area (i.e., a 70 mileradius area) may receive the data from the cloud 60. The vehicles mayreceive the information through wireless, Bluetooth, or othercommunications.

At 416, the cloud 60, or fog nodes, or local access points 68 monitorincoming data for notifications related to the outlaw vehicle. Forexample, data may be transferred to the cloud 60, fog nodes, or localaccess points 68 through wireless, Bluetooth, or other communications.The cloud 60, fog nodes, or local access points 68 may analyze the dataupon reception to determine wither the data includes any notificationsregarding the outlaw vehicle.

At 420, method 400 determines whether any package received. For example,as previously discussed in relation to steps 328 and 360 in method 300(FIG. 7), the communication unit 48 in vehicle 10 may take multiplepackages including timestamp, GPS data, and image data that apply to thesame incident classification (i.e., that apply to the identified outlawvehicle) and format the contents and data for distribution. The cloud60, fog nodes, or local access points 68 may monitor the data networksto determine whether any packages have been sent from local vehicles 10.If no packages have been received, method 400 returns to 416.

If packages have been received at 420, the packages are verified at 424.For example, the cloud 60, fog nodes, or local access points 68 mayanalyze the data in each package upon reception to determine whether thedata includes notifications regarding the outlaw vehicle. The cloud 60,fog nodes, or local access points 68 may also analyze the packages forauthenticity. For example the analysis may include verifying theencrypted vehicle ID, checksum of the report data, use of adecentralized certificate management authority system, etc.

At 428, the method 400 determines whether the package is valid. Forexample, the cloud 60, fog nodes, or local access points 68 may analyzethe data in each package to determine whether all necessary data (forexample, timestamp, GPS data, and image data for each image) is presentand whether all data is valid (for example, not corrupt). The cloud mayvalidate the data by comparing and corroborating the findings in aplurality of incident reports from multiple sources and observing eventsconsistent to the reported incident (e.g. broken down vehicle will causetraffic jams) or if the driver(s) reported or police already reportedthe event.

If the data is not valid, method 400 returns to 416. If it is not clearwhether the data is valid at 428, method 400 waits for more packages at432 and returns to 424. If the data is valid at 428, the police arenotified at 436. For example only, the packages may be sent to thepolice department through the fog node(s) or the cloud to notify thepolice of the outlaw vehicle sighting.

At 440, method 400 waits for additional packages. As in 416 and 432, thecloud 60, or fog nodes, or local access points 68 monitor incomingpackages containing data related to the outlaw vehicle. For example,data may be transferred to the cloud 60, fog nodes, or local accesspoints 68 through wireless, Bluetooth, or other communications.

At 444, the scene is reconstructed from the data in all packagesreceived from all local vehicles, mobile devices, or stationary devices10, 10′. The cloud 60, or fog nodes, or local access points 68 mayanalyze and compile all packages received that relate to a single outlawvehicle event to reconstruct as much of the timeframe surrounding theoutlaw vehicle sighting as possible.

At 448, the method 400 determines whether there is enough information toreconstruct the scene. For example, the cloud 60, or fog nodes, or localaccess points 68 may analyze the data to determine whether there are anymissing portions of data (for example, periods of time). If data ismissing, additional information is requested at 452 by pinging orpushing data requests to local vehicles, mobile devices, or stationarydevices 10′. Method 400 then returns to 444.

If there is enough information at 448, the scene reconstruction andpackaged data is sent for analysis at 456. For example, the data may besent to the police, government agency, or other service for analysis.

The method ends at 460.

Now referring to FIG. 9, an example method 500 for real-time locating ofan emergency vehicle is illustrated. Method 500 starts at 504. At 508vehicle 10 senses an emergency vehicle. For example, vehicle 10 maydetermine the presence of the emergency vehicle by sound, location,image, etc. Vehicle 10 further uses the sound, location, image, etc.,data to determine the type of emergency (for example, accident,distress, etc.). Upon sensing the emergency vehicle, vehicle 10 may alsoyield to the emergency vehicle if necessary.

At 512, the sensed information is sent to the cloud 60, fog node, orlocal access point 68. For example, the collected sound, location,image, type of vehicle, type of emergency, etc., information may be sentby wireless, Bluetooth, or other communications to the cloud 60, fognode, or local access point 68.

At 516, local vehicles, mobile devices, or stationary devices 10′ arenotified of the emergency. The cloud 60, fog node, or local access point68 may maintain and send out real-time updates to the location of theemergency vehicle. For example, the cloud 60, fog node, or local accesspoint 68 may send updates to the location of the emergency or emergencyvehicle to vehicles, mobile devices, or stationary devices 10′ in a20-mile radius of the emergency or emergency vehicle. Specifically, thecloud 60, fog node, or local access point 68 may send emergency vehiclelocation and instructions to local vehicle, mobile device, or stationarydevice 10′ in front of the emergency vehicle so that they can prepare toyield to the emergency vehicle. Additionally, the cloud 60, fog node, orlocal access point 68 may send updates to the location of the emergencyor emergency vehicle to mapping or navigation systems or services toupdate navigation maps to reflect the emergency,

At 520, method 500 ends.

The foregoing description is merely illustrative in nature and is in noway intended to limit the disclosure, its application, or uses. Thebroad teachings of the disclosure can be implemented in a variety offorms. Therefore, while this disclosure includes particular examples,the true scope of the disclosure should not be so limited since othermodifications will become apparent upon a study of the drawings, thespecification, and the following claims. It should be understood thatone or more steps within a method may be executed in different order (orconcurrently) without altering the principles of the present disclosure.Further, although each of the embodiments is described above as havingcertain features, any one or more of those features described withrespect to any embodiment of the disclosure can be implemented in and/orcombined with features of any of the other embodiments, even if thatcombination is not explicitly described. In other words, the describedembodiments are not mutually exclusive, and permutations of one or moreembodiments with one another remain within the scope of this disclosure.

Example embodiments are provided so that this disclosure will bethorough, and will fully convey the scope to those who are skilled inthe art. Numerous specific details are set forth such as examples ofspecific components, devices, and methods, to provide a thoroughunderstanding of embodiments of the present disclosure. It will beapparent to those skilled in the art that specific details need not beemployed, that example embodiments may be embodied in many differentforms and that neither should be construed to limit the scope of thedisclosure. In some example embodiments, well-known processes,well-known device structures, and well-known technologies are notdescribed in detail.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The terms “comprises,” “comprising,” “including,” and“having,” are inclusive and therefore specify the presence of statedfeatures, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. The method steps, processes, and operations described hereinare not to be construed as necessarily requiring their performance inthe particular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional or alternative steps may be employed.

When an element or layer is referred to as being “on,” “engaged to,”“connected to,” or “coupled to” another element or layer, it may bedirectly on, engaged, connected or coupled to the other element orlayer, or intervening elements or layers may be present. In contrast,when an element is referred to as being “directly on,” “directly engagedto,” “directly connected to,” or “directly coupled to” another elementor layer, there may be no intervening elements or layers present. Otherwords used to describe the relationship between elements should beinterpreted in a like fashion (e.g., “between” versus “directlybetween,” “adjacent” versus “directly adjacent,” etc.). As used herein,the term “and/or” includes any and all combinations of one or more ofthe associated listed items.

Although the terms first, second, third, etc. may be used herein todescribe various elements, components, regions, layers and/or sections,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer or section from another region,layer or section. Terms such as “first,” “second,” and other numericalterms when used herein do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer or section discussed below could be termed a second element,component, region, layer or section without departing from the teachingsof the example embodiments.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,”“lower,” “above,” “upper,” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. Spatiallyrelative terms may be intended to encompass different orientations ofthe device in use or operation in addition to the orientation depictedin the figures. For example, if the device in the figures is turnedover, elements described as “below” or “beneath” other elements orfeatures would then be oriented “above” the other elements or features.Thus, the example term “below” can encompass both an orientation ofabove and below. The device may be otherwise oriented (rotated 90degrees or at other orientations) and the spatially relative descriptorsused herein interpreted accordingly.

Spatial and functional relationships between elements (for example,between modules, circuit elements, semiconductor layers, units, etc.)are described using various terms, including “connected,” “engaged,”“coupled,” “adjacent,” “next to,” “on top of,” “above,” “below,” and“disposed.” Unless explicitly described as being “direct,” when arelationship between first and second elements is described in the abovedisclosure, that relationship can be a direct relationship where noother intervening elements are present between the first and secondelements, but can also be an indirect relationship where one or moreintervening elements are present (either spatially or functionally)between the first and second elements. As used herein, the phrase atleast one of A, B, and C should be construed to mean a logical (A OR BOR C), using a non-exclusive logical OR, and should not be construed tomean “at least one of A, at least one of B, and at least one of C.”

In the figures, the direction of an arrow, as indicated by thearrowhead, generally demonstrates the flow of information (such as dataor instructions) that is of interest to the illustration. For example,when element A and element B exchange a variety of information butinformation transmitted from element A to element B is relevant to theillustration, the arrow may point from element A to element B. Thisunidirectional arrow does not imply that no other information istransmitted from element B to element A. Further, for information sentfrom element A to element B, element B may send requests for, or receiptacknowledgements of, the information to element A.

In this application, including the definitions below, the term “module,”the term “unit,” or the term “controller” may be replaced with the term“circuit.” The term “module” or the term “unit” may refer to, be partof, or include: an Application Specific Integrated Circuit (ASIC); adigital, analog, or mixed analog/digital discrete circuit; a digital,analog, or mixed analog/digital integrated circuit; a combinationallogic circuit; a field programmable gate array (FPGA); a processorcircuit (shared, dedicated, or group) that executes code; a memorycircuit (shared, dedicated, or group) that stores code executed by theprocessor circuit; other suitable hardware components that provide thedescribed functionality; or a combination of some or all of the above,such as in a system-on-chip.

The module or unit may include one or more interface circuits. In someexamples, the interface circuits may include wired or wirelessinterfaces that are connected to a local area network (LAN), theInternet, a wide area network (WAN), or combinations thereof. Thefunctionality of any given module or unit of the present disclosure maybe distributed among multiple modules or units that are connected viainterface circuits. For example, multiple modules or units may allowload balancing. In a further example, a server (also known as remote, orcloud) module or unit may accomplish some functionality on behalf of aclient module or unit.

The term code, as used above, may include software, firmware, and/ormicrocode, and may refer to programs, routines, functions, classes, datastructures, and/or objects. The term shared processor circuitencompasses a single processor circuit that executes some or all codefrom multiple modules or units. The term group processor circuitencompasses a processor circuit that, in combination with additionalprocessor circuits, executes some or all code from one or more modulesor units. References to multiple processor circuits encompass multipleprocessor circuits on discrete dies, multiple processor circuits on asingle die, multiple cores of a single processor circuit, multiplethreads of a single processor circuit, or a combination of the above.The term shared memory circuit encompasses a single memory circuit thatstores some or all code from multiple modules or units. The term groupmemory circuit encompasses a memory circuit that, in combination withadditional memories, stores some or all code from one or more modules orunits.

The term memory circuit is a subset of the term computer-readablemedium. The term computer-readable medium, as used herein, does notencompass transitory electrical or electromagnetic signals propagatingthrough a medium (such as on a carrier wave); the term computer-readablemedium may therefore be considered tangible and non-transitory.Non-limiting examples of a non-transitory, tangible computer-readablemedium are nonvolatile memory circuits (such as a flash memory circuit,an erasable programmable read-only memory circuit, or a mask read-onlymemory circuit), volatile memory circuits (such as a static randomaccess memory circuit or a dynamic random access memory circuit),magnetic storage media (such as an analog or digital magnetic tape or ahard disk drive), and optical storage media (such as a CD, a DVD, or aBlu-ray Disc).

The apparatuses and methods described in this application may bepartially or fully implemented by a special purpose computer created byconfiguring a general purpose computer to execute one or more particularfunctions embodied in computer programs. The functional blocks andflowchart elements described above serve as software specifications,which can be translated into the computer programs by the routine workof a skilled technician or programmer.

The computer programs include processor-executable instructions that arestored on at least one non-transitory, tangible computer-readablemedium. The computer programs may also include or rely on stored data.The computer programs may encompass a basic input/output system (BIOS)that interacts with hardware of the special purpose computer, devicedrivers that interact with particular devices of the special purposecomputer, one or more operating systems, user applications, backgroundservices, background applications, etc.

The computer programs may include: (i) descriptive text to be parsed,such as HTML (hypertext markup language) or XML (extensible markuplanguage), (ii) assembly code, (iii) object code generated from sourcecode by a compiler, (iv) source code for execution by an interpreter,(v) source code for compilation and execution by a just-in-timecompiler, etc. As examples only, source code may be written using syntaxfrom languages including C, C++, C#, Objective-C, Swift, Haskell, Go,SQL, R, Lisp, Java®, Fortran, Perl, Pascal, Curl, OCaml, Javascript®,HTML5 (Hypertext Markup Language 5th revision), Ada, ASP (Active ServerPages), PHP (PHP: Hypertext Preprocessor), Scala, Eiffel, Smalltalk,Erlang, Ruby, Flash®, Visual Basic®, Lua, MATLAB, SIMULINK, and Python®.

None of the elements recited in the claims are intended to be ameans-plus-function element within the meaning of 35 U.S.C. § 112(f)unless an element is expressly recited using the phrase “means for,” orin the case of a method claim using the phrases “operation for” or “stepfor.”

The foregoing description of the embodiments has been provided forpurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure. Individual elements or featuresof a particular embodiment are generally not limited to that particularembodiment, but, where applicable, are interchangeable and can be usedin a selected embodiment, even if not specifically shown or described.The same may also be varied in many ways. Such variations are not to beregarded as a departure from the disclosure, and all such modificationsare intended to be included within the scope of the disclosure.

What is claimed is:
 1. An automatic external incident detection andreporting system for a vehicle, comprising: at least one of a pluralityof cameras and a plurality of proximity sensors; an incidentdetermination unit configured to receive signals from the at least oneof the plurality of cameras and the plurality of proximity sensors,detect whether an external incident has occurred, and determine a typeof incident; a remote vehicle or object position determination unitconfigured to receive signals from the at least one of the plurality ofcameras and the plurality of proximity sensors and determine an incidentlocation; and a communication unit configured to transmit at least theincident location and the type of incident to remote vehicles, to alocal access point, or to share the incident location and the type ofincident through a crowd-sourcing manner if the external incident hasoccurred.
 2. The system of claim 1, wherein the incident determinationunit is configured to use facial recognition, hand signals, or bodylanguage to determine whether a driver is in distress.
 3. The system ofclaim 1, wherein the incident determination unit is configured toanalyze images from the plurality of cameras to determine whether therehas been a traffic accident.
 4. The system of claim 1, wherein theincident determination unit is configured to either compare images fromthe plurality of cameras with known images of large animals to determinewhether there is large animal activity or use machine learning methodsand models to automatically identify one or more animals.
 5. The systemof claim 1, wherein the incident determination unit is configured toeither compare images from the plurality of cameras with known images ofan acceptable roadway to determine whether there are potholes or debrison a roadway or use machine learning methods and models to automaticallyidentify a road surface and any debris or damage to the road surface. 6.The system of claim 1, wherein the external incident is one of a vehicleaccident, an immobilized vehicle, a broken-down vehicle, a pothole,large animal activity, an outlaw vehicle, a driver or other person indistress, significant damage to a road, debris, and one or more personsloitering, and the external incident involves only one or more vehicles,persons, or objects outside of the vehicle.
 7. The system of claim 1,further comprising a vehicle position determination unit configured todetermine a current position of the vehicle.
 8. The system of claim 1,further comprising a driver alert system configured to alert a driverwhen an incident is detected by the incident determination unit.
 9. Thesystem of claim 1, wherein the communication unit is configured totransmit the incident location and type of incident to a datatransmission system on the vehicle, and the data transmission systemcommunicates the incident location and type of incident to a remoteserver.
 10. The system of claim 1, wherein the communication unit isconfigured to transmit the incident location and type of incident to anemergency services provider.
 11. A method for automatically detectingand reporting external incidents, comprising: receiving, by an incidentdetermination unit and a remote vehicle or object position determinationunit, a plurality of signals from at least one of a plurality of camerasand a plurality of proximity sensors disposed on a vehicle; detecting,by the incident determination unit, whether an external incident hasoccurred; determining, by the incident determination unit, a type ofincident; determining, by the remote vehicle or object positiondetermination unit, an incident location; and transmitting, by acommunication unit, at least the incident location and the type ofincident to remote vehicles or a local access point, or sharing, by thecommunication unit, the incident location and the type of incidentthrough a crowd-sourcing manner if the external incident has occurred.12. The method of claim 11, further comprising performing, by theincident determination unit, facial recognition, hand-signalrecognition, or body language recognition to determine whether a driveris in distress.
 13. The method of claim 11, further comprisinganalyzing, by the incident determination unit, images from the pluralityof cameras to determine whether there has been a traffic accident. 14.The method of claim 11, further comprising either comparing, by theincident determination unit, images from the plurality of cameras withknown images of large animals to determine whether there is large animalactivity, or employing, by the incident determination unit, detectionalgorithms to determine whether there is large animal activity.
 15. Themethod of claim 11, further comprising either comparing, by the incidentdetermination unit, images from the plurality of cameras with knownimages of an acceptable roadway to determine whether there are potholesor debris on the roadway, or employing, by the incident determinationunit, scene analysis algorithms to detect potholes or debris.
 16. Themethod of claim 11, wherein the external incident is one of a vehicleaccident, an immobilized vehicle, a broken-down vehicle, a majorpothole, large animal activity, an outlaw vehicle, a driver or otherperson in distress, significant damage to a road, and debris on theroad, and the external incident involves only one or more vehicles,persons, or objects outside of the vehicle.
 17. The method of claim 11,further comprising determining, by a vehicle position determinationunit, a current position of the vehicle.
 18. The method of claim 11,further comprising alerting, by a driver alert system, a driver when anincident is detected by the incident determination unit.
 19. The methodof claim 11, further comprising transmitting, by the communication unit,the incident location and type of incident to a data transmission systemon the vehicle, and communicating, by the data transmission system, theincident location and type of incident to a remote server.
 20. Themethod of claim 11, further comprising transmitting, by thecommunication unit, the incident location and type of incident to anemergency services provider.